Distributed Steganography in PDF Files—Secrets Hidden in Modified Pages

Koptyra, Katarzyna; Ogiela, Marek R.

doi:10.3390/e22060600

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…Various concealment methods, including steganography techniques, can be employed to hide information in PDF files, as elucidated in previous research [37]. In this study, the authors utilized tools such as pdftk and mergepdf to amalgamate PDFs, thereby creating a NewDataset through the fusion of malicious PDFs with non-malicious counterparts, as depicted in Figure 4.…”

Section: A New Dataset Creationmentioning

confidence: 99%

Designing A Pdf Malware Detection System Using Machine Learning

Wiharja,

Pradeka,

Suteddy

2024

politeknologi

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This research proposes an approach to build malicious PDF detection system using random forest algorithm, focusing the Evasive-PDFMal2022 dataset which is updated and extended with the addition of new datasets. This dataset includes malicious PDF files from CVE and Exploit-DB, non-malicious PDF files, as well as files from private collections and Technically-oriented PDF Collection. Features were extracted using the PDFID tool, resulting in 29 structural features that formed the basis for the Random Forest classification algorithm. Experiments showed that the model trained with the new dataset provided accuracy equivalent to the Evasive-PDFMal2022 model, at 98%, albeit with a small decrease in recall for the benign class. In addition, this research involved the creation of a website for metadata extraction and malicious PDF detection. Recognition goes to the dataset contributors, tool developers, and dataset providers from NIST and Exploit-DB. Overall, this research successfully increased the representation and diversity of the dataset, provided good model training results, improved detection from 3 malicious PDF variants to 13 variants, and created a practical tool for malicious PDF extraction and detection. Nonetheless, further development may be required to improve detection performance in more complex scenarios

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Section: A New Dataset Creationmentioning

confidence: 99%

Designing A Pdf Malware Detection System Using Machine Learning

Wiharja,

Pradeka,

Suteddy

2024

politeknologi

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“…Steganography also makes covert communications easier, especially when it comes to corporate and national security issues. This has potential uses in protecting confidential data [20]. It is also essential for the safe transportation of sensitive data [21].…”

Section: The Uses Of Steganographymentioning

confidence: 99%

A Deep Dive into Deep Learning-Powered Steganography for Enhanced Security: Review

Ahmed,

Jader Saeed

2024

IRJIET

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- Steganography—the practice of hiding data in digital media—has attracted a lot of interest because of its possible uses in secure communication. This study offers a thorough summary of the most recent developments in steganography, emphasizing the incorporation of deep learning (DL) methods in particular. The review explores several steganographic techniques, classifies them, and assesses the advantages and disadvantages of each. Moreover, it reviews current research projects that use DL to improve security and resilience in steganographic systems. This evaluation finds new trends, problems, and possibilities in the field by examining related studies. The major goal is to advance this field and broaden our understanding of steganography-based secure communication.

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“…For this reason, the most suitable are injection algorithms, which are independent of the content, and maybe in some applications, the substitution methods can also be used. An example of the recommended approach is to store the data block in the JPEG comment section [ 20 ]. It may be later pulled out in the same way as all other photo metadata.…”

Section: Creation Of Data Blocksmentioning

confidence: 99%

Imagechain—Application of Blockchain Technology for Images

Koptyra

Ogiela

2020

Sensors

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Imagechain is a cryptographic structure that chain digital images with hash links. The most important feature, which differentiates it from blockchain, is that the pictures are not stored inside the blocks. Instead, the block and the image are combined together in the embedding process. Therefore, the imagechain is built from standard graphic files that may be used in the same way as any other image, but additionally, each of them contains a data block that links it to a previous element of the chain. The presented solution does not require any additional files except the images themselves. It supports multiple file formats and embedding methods, which makes it portable and user-friendly. At the same time, the scheme provides a high level of security and resistance to forgery. This is achieved by hashing the whole file with embedded data, so the image cannot be altered or removed from the chain without losing integrity. This article describes the basic concept of an imagechain together with building blocks and applications. The two most important issues are embedding methods and block structure.

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Distributed Steganography in PDF Files—Secrets Hidden in Modified Pages

Cited by 10 publications

References 23 publications

Designing A Pdf Malware Detection System Using Machine Learning

Designing A Pdf Malware Detection System Using Machine Learning

A Deep Dive into Deep Learning-Powered Steganography for Enhanced Security: Review

Imagechain—Application of Blockchain Technology for Images

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